Autonomous vehicle is a modern vehicle which can sense ambient environment, make decision and judgment for scenarios and control itself without needing manual control. In the autonomous vehicle, a positioning module is a kernel fundamental module in an autonomous driving system, and not only an indispensable input of a path planning module but also a scenario understanding and classification algorithm simplifying a sensing module. In a current technical solution, a combination of an inertia navigation system and a laser point cloud positioning system is generally employed. At present, a positioning solution using an inertia navigation system mounted with a high-end IMU, and a laser point cloud positioning system is already applied to Google and Baidu autonomous vehicles.
The inertia navigation system provides an initial posture of the autonomous vehicle, including location information (planar location x, y and height z) and posture angles (roll, pitch and jaw). The laser point cloud positioning algorithm is employed to optimize the location information and the yaw.
When matched search is performed for the planar location x, y in the prior art, it is generally feasible to use metrics such as a reflection value and a height value, or individually use the reflection value, or individually use the height value, or use both of them in a simply-superimposed manner.
A drawback of individually considering a metric manner is that a wrong planar location x, y might be obtained when match distribution is undesirable.
The manner of simply superimposing the two to a certain degree overcomes a case of failure of a metric, but it also has a failure case. For example, as for a re-paved road surface, the distribution of reflection value match is very poor, the distribution of the height value match is better, and a final result of simply superimposing the two is relatively poor.
Therefore, individually considering a certain match or simply superimposing is not a good solution.